Abstract
Major Depressive Disorder (MDD), is a malady which has perturbed many around the globe. It has become a gigantic health concern for this world and economic burden globally. Traditionally, the clinicians use plethora of treatments to slow down the progression of this disease at early stage. This paper intent to better diagnose the depression; by transforming the Electroencephalogram (EEG) signals while using machine learning and deep learning technique. EEGs of 30 healthy and 34 Major Depressive Disorder (MDD) subjects were analyzed in this study. New features were generated from the preprocessed data and later various classifiers were used to predict the results. The finest accuracy of machine learning algorithm was acquired by K-nearest neighbors (KNN) which were 0.997; Decision tree (DT) acquired an accuracy of 0.984, Support vector machine (SVM) showed 0.957 accuracy while Naïve Bayes (NB) had an accuracy of 0.522. The proposed Deep Learning method which was convolutional neural network (CNN) resulted in the accuracy of 0.996. With these promising results this study proves a feasibility of EEG based Major Depressive Disorder diagnosis.
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Bashir, N., Narejo, S., Naz, B., Ali, A. (2022). EEG Based Major Depressive Disorder (MDD) Detection Using Machine Learning. In: Djeddi, C., Siddiqi, I., Jamil, A., Ali Hameed, A., Kucuk, İ. (eds) Pattern Recognition and Artificial Intelligence. MedPRAI 2021. Communications in Computer and Information Science, vol 1543. Springer, Cham. https://doi.org/10.1007/978-3-031-04112-9_13
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